SIA (Self Improving AI), released by Hexo Labs on May 26, 2026 , is the first open-source framework that co-evolves both an agent's scaffold and its model weights inside a single iterative loop. The MIT-licensed code is on github.com/hexo-ai/sia. This tutorial walks through the feedback loop logic, prerequisites, and a runnable five-generation LawBench experiment.
The Feedback Loop That Decides PPO, GRPO, or EAW
SIA's Feedback-Agent reads full execution trajectories, reward metrics, and task descriptions each generation, then decides whether the next step should be a scaffold edit, a LoRA weight update, or both — and selects the RL algorithm automatically based on the reward shape of the current task . Before SIA, harness-update systems (Darwin Gödel Machine, Hyperagents) and test-time training systems (TTRL, Discover-TTT) were entirely separate research directions. SIA is the first framework to combine both levers in a single self-improving loop, per the SIA paper (arXiv:2605.27276).
Quick Answer: SIA (arXiv:2605.27276, MIT license, May 2026) co-evolves agent scaffold and LoRA weights in a single loop. Run sia --task lawbench --max_gen 5; the Feedback-Agent picks PPO+GAE, GRPO, or Entropic Advantage Weighting based on reward shape — no RL algorithm choice required. On LawBench, the combined harness+weights variant reached 70.1% accuracy , 25.1 percentage points over prior SOTA.
